Now a days, Closed Circuit Television (CCTV) cameras are installedeverywhere in public places to monitor illegal activities like armedrobberies. Mostly CCTV footages are used as post evidence after theoccurrence of crime. In many cases a person might be monitoringthe scene from CCTV but the attention can easily drift on prolongedobservation. Eciency of CCTV surveillance can be improved by in-corporation of image processing and object detection algorithms intomonitoring process.The object detection algorithms, previously implemented in CCTVvideo analysis detect pedestrians, animals and vehicles. These algo-rithms can be extended further to detect a person holding weaponslike rearms or sharp objects like knives in public or restricted places.In this work the detection of weapon from CCTV frame is acquiredby using Histogram of Oriented Gradients (HOG) as feature vector andarticial neural networks performing back-propagation algorithm forclassication.As a weapon in the hands of a human is considered to be greaterthreat as compared to a weapon alone, in this work the detection ofhuman in an image prior to a weapon detection has been found advan-tageous. Weapon detection has been performed using three methods.In the rst method, the weapon in the image is detected directly with-out human detection. Second and third methods use HOG and back-ground subtraction methods for detection of human prior to detectionof a weapon. A knife and a gun are considered as weapons of inter-est in this work. The performance of the proposed detection methodswas analysed on test image dataset containing knives, guns and im-ages without weapon. The accuracy rate 84:6% has been achievedby a single-class classier for knife detection. A gun and a knife havebeen detected by the three-class classier with an accuracy rate 83:0%.

The current practice of construction productivity and emission monitoring is performed by either manual stopwatch studies which are significantly labor intensive and subject to human errors, or by the use of RFID and GPS tracking devices which may be costly and impractical. To address these limitations, a novel computer vision based method for automated 2D tracking, 3D localization, and action recognition of construction equipment from different camera viewpoints is presented. In the proposed method, a new algorithm based on Histograms of Oriented Gradients and hue-saturation Colors (HOG+C) is used for 2D tracking of the earthmoving equipment. Once the equipment is detected, using a Direct Linear Transformation followed by a non-linear optimization, their positions are localized in 3D. In order to automatically analyze the performance of these operations, a new algorithm to recognize actions of the equipment is developed. First, a video is represented as a collection of spatio-temporal features by extracting space-time interest points and describing each with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of these features by clustering their HOG descriptors. Equipment action categories are then learned using a multi-class binary Support Vector Machine (SVM) classifier. Given a novel video sequence, the proposed method recognizes and localizes equipment actions. The proposed method has been exhaustively tested on 859 videos from earthmoving operations. Experimental results with an average accuracy of 86.33% and 98.33% for excavator and truck action recognition respectively, reflect the promise of the proposed method for automated performance monitoring. / Master of Science

Radiologists are often plagued by limited time for completing their work, with an ever increasing workload. A picture archiving and communication system (PACS) is a platform for daily image reviewing that improves their work environment, and on that platform for example spinal MR images can be reviewed. When reviewing spinal images a radiologist wants vertebrae labels, and in Sectra's PACS platform there is a good opportunity for implementing an automatic method for spinal labeling. In this thesis a method for performing automatic spinal labeling, called a vertebrae classifier, is presented. This method should remove the need for radiologists to perform manual spine labeling, and could be implemented in Sectra's PACS software to improve radiologists overall work experience.Spine labeling is the process of marking vertebrae centres with a name on a spinal image. The method proposed in this thesis for performing that process was developed using a machine learning approach for vertebrae detection in sagittal MR images. The developed classifier works for both the lumbar and the cervical spine, but it is optimized for the lumbar spine. During the development three different methods for the purpose of vertebrae detection were evaluated. Detection is done on multiple sagittal slices. The output from the detection is then labeled using a pictorial structure based algorithm which uses a trained model of the spine to correctly assess correct labeling. The suggested method achieves 99.6% recall and 99.9% precision for the lumbar spine. The cervical spine achieves slightly worse performance, with 98.1% for both recall and precision. This result was achieved by training the proposed method on 43 images and validated with 89 images for the lumbar spine. The cervical spine was validated using 26 images. These results are promising, especially for the lumbar spine. However, further evaluation is needed to test the method in a clinical setting. / Radiologer får bara mindre och mindre tid för att utföra sina arbetsuppgifter, då arbetsbördan bara blir större. Ett picture archiving and communication system (PACS) är en platform där radiologer kan undersöka medicinska bilder, däribland magnetic resonance (MR) bilder av ryggraden. När radiologerna tittar på dessa bilder av ryggraden vill de att kotorna ska vara markerade med sina namn, och i Sectra's PACS platform finns det en bra möjlighet för att implementera en automatisk metod för att namnge ryggradens kotor på bilden. I detta examensarbete presenteras en metod för att automatiskt markera alla kotorna utifrån saggitala MR bilder. Denna metod kan göra så att radiologer inte längre behöver manuellt markera kotor, och den skulle kunna implementeras i Sectra's PACS för att förbättra radiologernas arbetsmiljö. Det som menas med att markera kotor är att man ger mitten av alla kotor ett namn utifrån en MR bild på ryggraden. Metoden som presenteras i detta arbete kan utföra detta med hjälp av ett "machine learning" arbetssätt. Metoden fungerar både för övre och nedre delen av ryggraden, men den är optimerad för den nedre delen. Under utvecklingsfasen var tre olika metoder för att detektera kotor evaluerade. Resultatet från detektionen är sedan använt för att namnge alla kotor med hjälp av en algoritm baserad på pictorial structures, som använder en tränad model för att kunna evaluera vad som bör anses vara korrekt namngivning. Metoden uppnår 99.6% recall och 99.9% precision för nedre ryggraden. För övre ryggraden uppnås något sämre resultat, med 98.1% vad gäller både recall och precision. Detta resultat uppnådes då metoden tränades på 43 bilder och validerades på 89 bilder för nedre ryggraden. För övre ryggraden användes 26 stycken bilder. Resultaten är lovande, speciellt för den nedre delen. Dock måste ytterligare utvärdering göras för metoden i en klinisk miljö.

This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.